Objective: This article includes the examination of potential methodological problems of the application of a forced choice response format in facial emotion recognition. 

Methodology: 33 subjects were presented with validated facial stimuli. The task was to make a decision about which emotion was shown. In addition, the subjective certainty concerning the decision was recorded. 

Results: The detection rates are 68% for fear, 81% for sadness, 85% for anger, 87% for surprise, 88% for disgust, and 94% for happiness, and are thus well above the random probability. 

Conclusion: This study refutes the concern that the use of forced choice formats may not adequately reflect actual recognition performance. The use of standardized tests to examine emotion recognition ability leads to valid results and can be used in different contexts. For example, the images presented here appear suitable for diagnosing deficits in emotion recognition in the context of psychological disorders and for mapping treatment progress.

The effect of forced choice on facial emotion recognition: a comparison to open verbal classification of emotion labels.

Detecting the emotional state of others from facial expressions is a key ability in emotional competence and several instruments have been developed to assess it. Typical emotion recognition tests are assumed to be unidimensional, use pictures or videos of emotional portrayals as stimuli, and ask the participant which emotion is depicted in each stimulus. However, using actor portrayals adds a layer of difficulty in developing such a test: the portrayals may fail to be convincing and may convey a different emotion than intended. For this reason, evaluating and selecting stimuli is of crucial importance. Existing tests typically base item evaluation on consensus or expert judgment, but these methods could favor items with high agreement over items that better differentiate ability levels and they could not formally test the item pool for unidimensionality. To address these issues, the authors propose a new test, named Facial Expression Recognition Test (FERT), developed using an item response theory two-parameter logistic model. Data from 1,002 online participants were analyzed using both a unidimensional and a bifactor model, and showed that the item pool could be considered unidimensional. The selection was based on the items' discrimination parameters, retaining only the most informative items to investigate the latent ability. The resulting 36-item test was reliable and quick to administer. The authors found both a gender difference in the ability to recognize emotions and a decline of such ability with age. The PsychoPy implementation of the test and the scoring script are available on a Github repository. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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Development and validation of the Facial Expression Recognition Test (FERT).

As the abilities in facial emotion recognition continue to provide relevant clinical information as well as organizational decision makers the need for valid and reliable instruments to assess the success of standardized systems for measuring facial emotion recognition is increasingly important. In this study 59 participants underwent two administrations of the “Pictures of Facial Affect-Ulm” for measuring facial emotion recognition abilities with an average time interval of 7.06 days (SD = 2.91). Results revealed that PFA-U provides high test retest-reliability that can be used for defining pre- and post-intervention scores in a wide range of application areas (e.g. performance in facial emotion recognition for patients with neurological or mental disorders in therapeutic setting).

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Papers

The Influence of Naturalness, Attractiveness and Intensity on Facial Emotion Recognition